Explanations

Explanation is discussion that clarifies and illuminates a particular topic. Explanation is understanding-oriented.

Architectural Overview

The diagram illustrates the components of a Konfuzio Server deployment. Optional components are represented by dashed lines. The numbers in brackets represent the minimal and maximal container count per component.

graph TD classDef client fill:#D5E8D4,stroke:#82B366,color:#000000; classDef old_optional fill:#E1D5E7,stroke:#9673A6,color:#000000; classDef optional fill:#DAE8FC,stroke:#6C8EBF,color:#000000,stroke-dasharray: 3 3; ip("Loadbalancer / Public IP") smtp("SMTP Mailbox") a("Database") b("Task Queue") c("File Storage") worker("Generic Worker (1:n)") web("Web & API (1:n)") beats("Beats Worker (1:n)") mail("Mail-Scan (0:1)") %% Outside references smtp <-- Poll emails --> mail ip <--> web %% Optional Containers ocr("OCR (0:n)") segmentation("Segmentation (0:n)") summarization("Summarization (0:n)") flower("Flower (0:1)") %% Server / Cluster h0("Server 1") h1("Server 2") h2("Server 3") h3("Server 4") h4("Server 5") i("...") j("Server with GPU") subgraph all["Private Network"] subgraph databases["Persistent Container / Services"] a c b end subgraph containers["Stateless Containers"] mail web flower worker beats subgraph optional["Optional Containers"] ocr segmentation summarization end end subgraph servers["Server / Cluster"] h0 h1 h2 h3 h4 i j end worker <--> databases worker -- Can delegate tasks--> optional worker -- "Can process tasks"--> worker web <--> databases web <--> flower flower <--> b mail <--> databases beats <--> databases containers -- "Operated on"--> servers databases -- "Can be operated on"--> servers end click flower "/web/on_premises.html#optional-6-use-flower-to-monitor-tasks" click web "/web/on_premises.html#start-the-container" click worker "/web/on_premises.html#start-the-container" click ocr "/web/on_premises.html#optional-7-use-azure-read-api-on-premise" click segmentation "/web/on_premises.html#optional-8-install-document-segmentation-container" click summarization "/web/on_premises.html#optional-9-install-document-summarization-container" class flower optional class ocr optional class mail optional class ocr optional class segmentation optional class summarization optional class h1 optional class h2 optional class h3 optional class h4 optional class i optional class j optional

OCR Processing

After uploading a document, depending on how your project was set up, the document is automatically queued and processed by our OCR. In the below breakdown we try to demystify this process, and give you some insight into all the steps which happen during OCR processing.

Project settings

We first look at the projects settings to see what base settings have been set up for your documents.

  1. Chosen OCR engine

    1. easy

    2. precise

  2. Chosen processing type

    1. OCR

    2. Embedding

    3. Embedding and OCR

  3. Chosen auto-rotation option

    (Only available for precise OCR engine)

    1. None

    2. Rounded

    3. Exact

File pre-processing

During file upload, after the Project settings have been evaluated, we look at the file:

  1. We check if the filetype is supported.

  2. We check if the file is valid and/or corrupted.

  3. If the file is corrupted, some repairing is attempted.

  4. We check if the filetype provides embeddings.

  5. We check if the project enforces OCR.

  6. We then conduct OCR on the file.

  7. We check if the image is angled.

  8. We create thumbnails per page.

  9. We create images per page.

OCR Text extraction

During evaluation of both project settings and file, we also process OCR extraction

  1. We use the chosen engine on the pre-processed file.

    1. If “Embedding and OCR” is chosen, internally we check which processing type is the most suitable, and use either Embedding or OCR

    2. Depending on chosen processing type, some pre-processing may be done:

      1. Convert non-PDF Documents to a PDF that is being used here

      2. Convert PDF to text (in case of embeddings)

    3. If some sort of PDF corruption is detected, within our ability we attempt to repair the PDF

    4. If the PDF or TIFF is multi page (and valid) we split the document in pages and process each page separately

  2. We check whether auto-rotation was chosen when the precise OCR engine is used]

    1. If rounded angle correction was chosen, we rotate the image to the nearest 45/90 degrees.

    2. If exact angle rotation was chosen, we rotate the image at its exact angle rotation value.

  3. We attempt to extract the text from (either ocr, embedded or both)

    1. OCR may fail because text on the document is technically unreadable, the file is corrupted or empty and cannot be repaired

    2. OCR may fail because engine does not support the text language

Finally, we return you the extracted text.

Background processes

Processes within our server are distributed between Celery workers between several tasks. Together this creates the definition of Servers internal workflow. Below the individual tasks of the Server’s workflow are described in order of their triggered events. Tasks are run in parallel queue’s which are grouped in celery chords. While some tasks in each queue run in parallel, some tasks are still dependent on others. And no next queue will start until all tasks in the queue are finished. More on Celery workflows can be found here: https://docs.celeryq.dev/en/stable/userguide/canvas.html

Queue’s

id

queue-name

description

1

ocr

ocr and post ocr tasks

2

processing

non dependent tasks

3

categorize

categorization tasks

4

extract

extraction after ocr

5

finalize

end queue after OCR and extraction has occurred

6

training

queue for AI training

7

training_heavy

queue for RAM intensive ai training

8

evaluation

queue for AI evaluation

Celery Tasks

Document tasks

Series of events & tasks triggered when uploading a Document

Queue

task id

task name

description

default time limit

1, 2

1

page_ocr

Apply OCR to the documents page(s).

10 minutes

1, 2

2

page_image

Create png image for a page. If a PNG was submitted or already exists it will be returned without regeneration.

10 minutes

1

3

set_document_text_and_bboxes

Collect the result of the pages OCR (OCR from task_id #1) and set text & bboxes.

1 minute

3

4

categorize

Categorize the document.

3 minutes

4

5

document_extract

Extract the document using the AI models linked to the Project.

60 minutes

5

6

build_sandwich

Generates the pdfsandwich for a submitted PDF

30 minutes

5

7

generate_entities

Generate entities for a document which are shown in the labeling tool.

60 minutes

5

8

set_labeling_available

Sets the document available for labeling

10 minutes

5

9

get_hocr

Get hOCR representation for bboxes (bboxes from task_id #3).

5 minutes

Extraction & Category AI Training

Extraction AI

Series of events triggered when training an extraction AI

Queue

task id

task name

description

default time limit

2

1

page_image

Create png image for a page. If a PNG was submitted or already exists it will be returned without regeneration.

10 minutes

6, 7

1

train_extraction_ai

Start the training of the Ai model.

20 hours

4

2

document_extract

Extract the document using the AI models linked to the Project.

60 minutes

6, 7, 8

3

evaluate_ai_model

Evaluate the trained Ai models performance.

60 minutes
Category AI

Series of events triggered when training a Categorization AI

Queue

task id

task name

description

default time limit

8

2

train_category_ai

Start the training of the categorization model.

10 hours

8, 3

3

categorize

Run the categorization against all Documents in the its category.

3 minutes

6, 7, 8, 3

4

evaluate_ai_model

Evaluate the categorization Ai models performance.

60 hours